## Research

**Projects and collaboration:**

**Causal inference:**

– Causal inference with missing values (with Stefan Wager)

– Transporting causal effect, combining RCT and observational data (with Shu Yang),

– Survival causal inference, sensitivity analyses, policy learning (with Antoine Chambaz)

**Missing values:**

– Missing Non At Random data (with Claire Boyer)

– Supervised Learning with missing values: Random Forests, Neural Nets (with Erwan Scornet, Gael Varoquaux and Marine Le Morvan)

– Variable selection to control the FDR with missing values (with Gosia Bogdan)

– PCA with missing values, multiple imputation, package missMDA (with François Husson)

**Health applications:**

– Handling severe trauma patients, with the Traumabase group, J.P Nadal and Capgemini

– Covid19: Application for bed allocation monitoring/Predict need of intubation/ Effect of hydrochloroquine

**Others:**

– New collaborations: with Jes Frellsen with a grant

– Distributed computation with hospital data (with Balasubramanian Narasimhan)

– Exploratory data analysis (What was the French school of data analysis?)

S**tudents & group’s meeting: the missing data and causal inference group at Inria**

**Associate Editor: **Past: Journal of Computational & Graphical Statistics. Journal of Statistical Software. (7 years). AC for Neurips, ICLR.

An overview of my research up to 2016 can be found in my Habilitation. (slides)

##### Publications:

Year | Authors | Title | link |
---|---|---|---|

2023 | Zhao, P., Gatulle, N, James, A., Josse, J. & Chambaz, A. | ||

2023 | Bénard, C., Naf, J. & Josse, J. | MMD-based Variable Importance for Distributional Random Forest | |

2023 | Sussman, H., Chambaz, A. & Josse, J. | Adaptive Conformal, an R package for adaptive conformal inference | |

2023 | Zhao, P., Chambaz, A., Josse, J., Yang, S. | Positivity-free Policy Learning with Observational Data | |

2023 | Bénard, C, Josse, J. | Variable importance for causal forests: breaking down the heterogeneity of treatment effects | |

2023 | Colnet, B, Josse, J., Varoquaux, G., Scornet, E. | Risk ratio, odds ratio, risk difference... Which causal measure is easier to generalize? Submitted. | |

2023 | Zaffran, Josse, J. M., Dieuleveut A., Romano, Y. | Conformal prediction with missing values. ICML2023. | pdf poster |

2023 | Zhao, P., Josse, J. & Yang, S. | (2023). Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival data. Submitted. | |

2022 | Colnet, B, Josse, J., Varoquaux, G., Scornet, E. | Reweighting the RCT for generalization: finite sample analysis and variable selection. Submitted. | |

2022 | Blet et al. | Association between in-ICU red blood cells transfusion and one-year mortality in ICU survivors. Critical Care. | |

2022 | Colnet, B, Josse, J., Varoquaux, G., Scornet, E. | Generalizing a causal effect: sensitivity analysis and missing covariates. Journal of Causal Inference. | pdf slides |

2022 | Gauss et al. | Is Early Norepinephrine Associated with 24-hour Mortality of Blunt Trauma Patients in Haemorrhagic Shock? An International Cohort Study. Jama Network. | |

2022 | Garaix et al. | Decision-making tools for healthcare structures in times of pandemic. Anaesthesia Critical Care & Pain Medicine. | |

2022 | Zaffran et al. | Adaptive conformal prediction for time series.ICML2022. | pdf slides video |

2022 | Perez-Lebel et al. | Benchmarking missing-values approaches for predictive models on health databases.GigaScience. | |

2021 | Le Morvan, J. Josse, E. Scornet. & G. Varoquaux | What’s a good imputation to predict with missing values? Neurips 2021. (Spotlight). | pdf video slides |

2021 | Sportisse, A. et al. | Model-based Clustering with Missing Not At Random Data. Submitted. | |

2021 | Mayer, I., Josse, J & Traumbase | Transporting treatment effects with incomplete attributes. Biometrical Journal | |

2020-2023 | Colnet, B et al. | Causal inference methods for combining randomized trials and observational studies: a review. Statistical Science. | |

2020 | Le Morvan, J. Josse, M., Moreaux, T, E. Scornet. & G. Varoquaux | Neumiss networks: differential programming for supervised learning with missing values. Neurips2020. (Oral) | pdf slides video slides video |

2020 | Sbidian et al. | Hydroxychloroquine with or without azithromycin and in-hospital mortality or discharge in patients hospitalized for COVID-19 infection: a cohort study of 4,642 in-patients in France. Preprint. | |

2020 | Consortium ICUBAM | ICU Bed Availability Monitoring and analysis in the Grand Est région of France during the COVID-19 epidemic. Statistiques et Société. | pdf slides |

2020 | A. Sportisse, C. Boyer, and Josse, J. | Estimation and imputation in Probabilistic Principal Component Analysis with Missing Not At Random data.Neurips2020. | pdf slides video code |

2020 | A. Sportisse, C. Boyer, A. Dieuleveut, J. Josse. | Debiasing Stochastic Gradient Descent to handle missing values. Neurips2020. | pdf slides |

2020 | J.D. Moyer et al. | Trauma reloaded: Trauma registry in the era of data science. Anaesthesia Critical Care & Pain Medicine. | |

2020 | Muzellec, B., Josse, J. Boyer, C. & Cuturi, M. | Missing Data Imputation using Optimal Transport.ICML2020. | pdf slides videos code |

2019 | Josse, J., Mayer, I, & Vert, J.P. | MissDeepCausal: causal inference from incomplete data using deep latent variable models. Preprint. | pdf |

2020 | Le Morvan, M., N. Prost, J. Josse, E. Scornet. & G. Varoquaux | Linear predictor on linearly-generated data with missing values: non consistency and solutions. AISTAT2020. | pdf slides |

2020 | Descloux, P. , Boyer, C. Josse, J. Sportisse, A. Sardy, S. | Robust Lasso-Zero for sparse corruption and model selection with missing covariates. Scandinavian Journal of Statistics. | |

2022 | Mayer, I, Sportisse, A., Josse, J., Vialaneix, N., Tierney, N. | R-miss-tastic: a unified platform for missing values methods and workflows. R journal. | |

2019-20 | Mayer, I, Josse, J., Wager, S., Sverdr, E., Moyer, J.D. and Gauss, T. | Doubly robust treatment effect estimation with incomplete confounders. Annals Of Applied Statistics. | pdf code slides videos |

2019-21 | M. Bogdan, W. Jiang, J. Josse, B. Miasojedow and V. Rockova. | Adaptive Bayesian SLOPE – High dimensional Model Selection with Missing Values. Journal of Computational and Graphical Statistics. | pdf slides |

2019 | Josse, J., Prost, N., Scornet, E. & Varoquaux, G. | On the consistency of supervised learning with missing values. Preprint. | pdf slidescode slides |

2019 | G. Robin, O. Klopp, J. Josse, E. Moulines, and R. Tibshirani | Main effects and interactions in mixed and incomplete data frames.Journal of the American Statistical Association. | pdf Package |

2019 | Hamada, S et al. | Effect of Fibrinogen administration on early mortality in traumatic haemorrhagic shock: a propensity score analysis. Journal of Trauma. | |

2019 | Sportisse, A., Boyer, C. and Josse, J. | Low-rank estimation with missing non at random data. Statistics and Computing. | pdf code |

2018 | Josse, J., Husson, F. Robin, G. and Balasubramanian. N. | Imputation of mixed data with multilevel SVD. Journal of Computational and Graphical Statistics. | pdf slides |

2018 | Robin, G, Sardy, S., Moulines, E. and Josse, J. | Low-rank model with covariates for count data with missing values. Journal of Multivariate Analysis. | pdf Package code |

2018 | Jiang, W., Lavielle, M. Josse, J. and T. Gauss. | Logistic Regression with Missing Covariates -- Parameter Estimation, Model Selection and Prediction within a Joint-Modeling Framework. CSDA. | pdf slides Package, code |

2018 | G. Robin, Hoi To Wai, J. Josse, O. Klopp and E. Moulines | Low-rank interactions and sparse additive effects model for large data frames. NeurIPS 2018. | |

2018 | Josse, J. and Reiter, J. | Introduction to the Special Section on Missing Data. Statistical Sciences. | |

2018 | Seijo-Pardo, B., Alonso-Betanzos, A., P. Bennett, K. Bol\'on-Canedo, Josse, J., Saeed, M., Guyon, I. | Feature selection in the presence of missing data. Neurocomputing, ESANN. | |

2017-2018 | Mozharovskyi, P., Husson, F. and Josse, J. | Nonparametric imputation by data depth. Journal of the American Statistical Association. | pdf slides code |

2017 | Holmes, S and Josse, J. | 50 years of data-sciences, discussion.Journal of Computational and Graphical Statistics. | |

2017 | Bollmann, S., Cook, Di. Dumas, J., Fox, J., Josse, J., Keyes, O. Strobl, C., Turner, H. and Debelak, R. | A First Survey on the Diversity of the R Community. R journal. | pdf slides |

2017 | G. Celeux, J. Jewson, J. Josse, J.M. Marin and C. P. Robert. | Some discussions on the Read Paper "Beyond subjective and objective in statistics" by A. Gelman and C. Hennig. | |

2017 | Foulley, JL, Celeux, G and Josse, J. | Empirical Bayes approaches to PageRank type algorithms for rating scientific journals. Technical report. | pdf slides |

2016 | Sobczyk, P, Bogdan, M. and Josse, J. | PCA using penalized semi-integrated likelihood. Journal of Computational and Graphical Statistics. | |

2016 | Fithian, W. and Josse, J. | Multiple Correspondence Analysis & the Multilogit Bilinear Model. Journal of Multivariate Analysis. | pdf slides |

2016 | Husson, F., Josse, J. and Saporta, G. | Jan de Leeuw and the French school of data analysis. Journal of Statistical Software. | |

2016-2017 | Josse, J., Sardy, S. and Wager, S. | denoiseR: a package for low rank matrix estimation. Preprint. | pdf Package |

2016 | Groenen, P. and Josse, J. | Multinomial Multiple Correspondence Analysis. Preprint. | |

2016 | Fujii, H., Josse, J., Tanioka, M., Miyachi, Y. Husson, F., and Ono, M. | Regulatory T cells in melanoma revisited by a computational clustering of FOXP3+ T cell subpopulations.Journal of Immunology. | |

2015 | Audigier, V., Husson, F. and Josse, J. | MIMCA: Multiple imputation for categorical variables with multiple correspondence analysis.Statistics and Computing. | pdf slides |

2015-2016 | Josse, J and Wager, S. | Bootstrap-Based Regularization for Low-Rank Matrix Estimation. Journal of Machine Learning research. | pdf slides |

2015 | Josse, J. and Sardy, S. | Adaptive Shrinkage of singular values. Statistics and Computing. | |

2015 | Josse, J and Husson, F. | missMDA a package to handle missing values in and with multivariate data analysis methods.Journal of Statistical Software. | |

2015 | Audigier, V., Husson, F. and Josse, J. | Multiple Imputation with Bayesian PCA. Journal of Statistical Computation and Simulation. | |

2015-2016 | Josse, J. and Holmes, S. | Measuring multivariate association.Statistics Survey. | |

2014 | Audigier, V., Husson, F. and Josse, J. | A principal components method to impute mixed data. Advances in Data analysis and Classification. | pdf slides |

2014 | Josse, J., Wager, S. and Husson, F. | Confidence areas for fixed-effects PCA. Journal of Computational and Graphical Statistics. | pdf slides |

2014 | Dray, S and Josse, J. | Principal component analysis with missing values: a comparative survey of methods. Plant Ecology. | |

2014 | Josse, J., van Eeuwijk, F., Piepho, H-P and Denis, J.B. | Another look at Bayesian analysis of AMMI models for genotype-environment data. Journal of Agricultural, Biological, and Environmental Statistics. | |

2013 | Verbanck, M., Josse, J. and Husson, F. | Regularized PCA to denoise and visualise data. Statistics and Computing. | |

2013 | Josse, J., Timmerman, M.E. and Kiers, H.A.L. | Missing values in multi-level simultaneous component analysis. Chemometrics and Intelligent Laboratory Systems. | |

2013 | Husson, F. and Josse, J. | Handling missing values in Multiple Factor Analysis. Food Quality and Preferences. | |

2013 | Josse, J and Husson, F. | Handling missing values in exploratory multivariate data analysis methods. Journal de la SFdS. Paper written for the best Ph.D doctoral thesis prize delivered by the French Statistical Society. | |

2012 | Josse, J., Chavent, M., Liquet, B. and Husson, F. | Regularized Iterative Multiple Correspondence Analysis. Journal of Classification. | |

2011 | Josse, J and Husson, F. | Selecting the number of components in PCA using cross-validation approximations. Computational Statistics and Data Analysis. | |

2011 | Josse, J., Husson, F. and Pagès, J. | Multiple imputation in PCA. Advances in data analysis and classification. | |

2010 | Josse, J., Husson, F. and Pagès, J. | Principal component methods - hierarchical clustering - partitional clustering: why would we need to choose for visualizing data? Technical report. | |

2009 | Josse, J., Husson, F. and Pagès, J. | Analyse en Composantes Principales. Journal de la SFdS. | |

2008 | Josse, J., Husson, F. and Pagès, J. | Testing the significance of the RV coefficient. Computational Statistics and Data Analysis. | |

2008 | Lê S., Josse, J. and Husson, F. | FactoMineR: an R package for multivariate analysis.Journal of Statistical Software. | |